Artificial Intelligence Assisted Data Engineering Techniques for Real-Time Data Quality Management and Predictive Analytics

Mitchell Ghemawat

Authors

Mitchell Ghemawat Predictive Analytics Specialist, United Kingdom. Author

Keywords:

Artificial Intelligence, Data Engineering, Data Quality Management, Predictive Analytics, Machine Learning, Real-Time Processing, Big Data, Data Governance, Stream Analytics

Abstract

The rapid growth of digital technologies, cloud computing, Internet of Things (IoT), and enterprise applications has led to the generation of massive volumes of data. Organizations increasingly rely on data-driven decision-making to gain competitive advantages, improve operational efficiency, and enhance customer experiences. However, the effectiveness of data analytics depends heavily on the quality, accuracy, consistency, and reliability of data. Traditional data engineering approaches often struggle to manage large-scale, high-velocity data streams while maintaining data quality in real time. Artificial Intelligence (AI) has emerged as a transformative technology that enhances data engineering processes through intelligent automation, anomaly detection, predictive maintenance, and adaptive data management. This research paper examines AI-assisted data engineering techniques for real-time data quality management and predictive analytics, focusing on developments and methodologies established. The study explores machine learning algorithms, deep learning frameworks, automated data pipelines, stream processing architectures, and intelligent data governance systems that contribute to improved data quality and predictive decision-making. Furthermore, the paper discusses challenges, benefits, practical applications, and future research directions in AI-enabled data engineering environments.

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